MODELING OF A FLUIDIZED-BED DRIER USING ARTIFICIAL NEURAL-NETWORK

Proper modelling of a fluidized bed drier (FBD) is important to design model based control strategies. A FBD is a non-linear multivariable s ystem with non-minimum phase characteristics. Due to the complexities in FBD conventional modelling techniques are cumbersome. Artificial ne ural network (ANN) with its inherent ability to ''learn'' and ''absorb '' non-linearities, presents itself as a convenient tool for modelling such systems. In this work, an ANN model for continuous drying FBD is presented. A three layer fully connected feedfordward network with th ree inputs and two outputs is used. Backpropagation learning algorithm is employed to train the network. The training data is obtained from computer simulation of a FBD model from published literature. The trai ned network is evaluated using randomly generated data as input and ob served to predict the behaviour of FBD adequately.